SWOT Simulated North American Continent Hydrology Dataset Exploration in the Cloud

From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.

Accessing and Visualizing SWOT Simulated Datasets

Requirement:

This tutorial can only be run in an AWS cloud instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via earthaccess python library; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.

Learning Objectives:

• Access all 5 products of SWOT HR sample data (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine
• Visualize accessed data

SWOT Simulated Level 2 North America Continent KaRIn High Rate Version 1 Datasets:

1. River Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1

DOI: https://doi.org/10.5067/KARIN-2RSP1

2. Lake Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1

DOI: https://doi.org/10.5067/KARIN-2LSP1

3. Water Mask Pixel Cloud NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1

DOI: https://doi.org/10.5067/KARIN-2PIX1

4. Water Mask Pixel Cloud Vector Attribute NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1

DOI: https://doi.org/10.5067/KARIN-2PXV1

5. Raster NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1

DOI: https://doi.org/10.5067/KARIN-2RAS1

Notebook Author: Cassie Nickles, NASA PO.DAAC (Aug 2022)

Libraries Needed

import glob
import os
import requests
import s3fs
import netCDF4 as nc
import h5netcdf
import xarray as xr
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import hvplot.xarray
import shapefile as shp
import zipfile
import earthaccess
from earthaccess import Auth, DataCollections, DataGranules, Store

Earthdata Login

An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below.

auth = earthaccess.login(strategy="interactive", persist=True)

Set up an s3fs session for Direct Access

s3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the data access information.

fs_s3 = earthaccess.get_s3fs_session(daac='PODAAC', provider='POCLOUD')

Single File Access

The s3 access link can be found using earthaccess data search. Since this collection consists of Reach and Node files, we need to extract only the granule for the Reach file. We do this by filtering for the ‘Reach’ title in the data link.

Alternatively, Earthdata Search (see tutorial) can be used to manually search for a single file.

1. River Vector Shapefiles

#retrieves granule from the day we want
river_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1', temporal = ('2022-08-22 19:24:41', '2022-08-22 19:30:37'))

Granules found: 2

#finds the s3 link of the one granule we want (The collection contains both Reaches and Nodes, but here we want only the Reach)
river_data = []
for g in river_results:
    for l in earthaccess.results.DataGranule.data_links(g, access='direct'):
        if "Reach" in l:
            river_data.append(l)
print(river_data)

['s3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.zip']

#creates s3 file object for the one link we want
s3_file_obj1 = fs_s3.open(river_data[0], mode='rb')

The native format for this sample data is a .zip file, and we want the .shp file within the .zip file, so we need to download the contents of the zip file into the cloud environment. I created a folder called SWOT_HR_shp to write to. Change the path to where you would like your extracted files to be written.

with zipfile.ZipFile(s3_file_obj1, 'r') as zip_ref:
    zip_ref.extractall('SWOT_HR_shp')

Next, we’ll look at the attribute table of the .shp file we just extracted to the ‘SWOT_HR_shp’ folder.

SWOT_HR_shp1 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.shp') 
SWOT_HR_shp1

	reach_id	time	time_tai	time_str	p_lat	p_lon	river_name	wse	wse_u	wse_r_u	...	p_width	p_wid_var	p_n_nodes	p_dist_out	p_length	p_maf	p_dam_id	p_n_ch_max	p_n_ch_mod	geometry
0	71224300241	7.145115e+08	7.145114e+08	2022-08-22T19:2441Z	49.364818	-94.879318	no_data	3.472248e+01	-1.000000e+12	1.511000e-02	...	7294.5	3.265803e+06	15	390935.258	3008.959150	-1.000000e+12	0	15	8	LINESTRING (-94.86483 49.37485, -94.86515 49.3...
1	71224300253	7.145115e+08	7.145115e+08	2022-08-22T19:2446Z	49.049486	-94.899554	no_data	3.439994e+01	-1.000000e+12	7.600000e-03	...	394.5	7.876447e+06	42	444613.943	8411.845753	-1.000000e+12	0	8	1	LINESTRING (-94.92557 49.08401, -94.92556 49.0...
2	71224300263	7.145115e+08	7.145115e+08	2022-08-22T19:2448Z	48.977915	-94.869598	no_data	3.434701e+01	-1.000000e+12	9.620000e-03	...	6365.5	2.935181e+06	42	453020.631	8406.687501	-1.000000e+12	0	3	1	LINESTRING (-94.88015 49.01512, -94.88006 49.0...
3	71224300273	7.145115e+08	7.145115e+08	2022-08-22T19:2449Z	48.902998	-94.854720	no_data	3.416786e+01	-1.000000e+12	1.372000e-02	...	4650.0	3.770782e+06	43	461636.940	8616.309267	-1.000000e+12	0	1	1	LINESTRING (-94.86229 48.94092, -94.86228 48.9...
4	71224300283	7.145115e+08	7.145115e+08	2022-08-22T19:2450Z	48.883377	-94.783621	no_data	3.426341e+01	-1.000000e+12	5.050000e-03	...	10439.0	2.952077e+07	46	470821.047	9184.106587	-1.000000e+12	0	5	1	LINESTRING (-94.73002 48.90430, -94.72952 48.9...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
666	74291700141	7.145116e+08	7.145116e+08	2022-08-22T19:2729Z	39.811344	-92.684233	no_data	3.869180e+01	-1.000000e+12	3.840000e-01	...	42.0	1.060619e+02	48	2463828.961	9553.659853	-1.000000e+12	0	1	1	LINESTRING (-92.68784 39.77391, -92.68804 39.7...
667	74291700151	7.145116e+08	7.145116e+08	2022-08-22T19:2727Z	39.888856	-92.683047	no_data	3.687848e+01	-1.000000e+12	1.897500e-01	...	42.0	1.208373e+02	48	2473386.489	9557.528221	-1.000000e+12	0	1	1	LINESTRING (-92.68497 39.84931, -92.68497 39.8...
668	74291700161	-1.000000e+12	-1.000000e+12	no_data	39.962507	-92.671510	no_data	-1.000000e+12	-1.000000e+12	-1.000000e+12	...	42.0	9.634731e+01	48	2482916.982	9530.492810	-1.000000e+12	0	1	1	LINESTRING (-92.66461 39.92629, -92.66425 39.9...
669	74291700171	7.145116e+08	7.145116e+08	2022-08-22T19:2725Z	40.045903	-92.690485	no_data	3.647532e+01	-1.000000e+12	1.394000e-01	...	54.0	6.557368e+02	59	2494780.714	11863.732260	-1.000000e+12	0	2	1	LINESTRING (-92.68041 40.00013, -92.68006 40.0...
670	74291700181	7.145116e+08	7.145116e+08	2022-08-22T19:2723Z	40.133798	-92.687305	no_data	3.384407e+01	-1.000000e+12	1.019290e+00	...	42.0	1.250056e+02	58	2506424.880	11644.165636	-1.000000e+12	0	1	1	LINESTRING (-92.68547 40.09150, -92.68519 40.0...

671 rows × 111 columns

fig, ax = plt.subplots(figsize=(11,7))
SWOT_HR_shp1.plot(ax=ax, color='black')

<Axes: >

2. Lake Vector Shapefiles

The lake vector shapefiles can be accessed in the same way as the river shapefiles above.

lake_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1', temporal = ('2022-08-22 19:24:18', '2022-08-22 19:30:50'))

Granules found: 3

#find the s3 link of the desired granule (This collection has three options: Obs, Unassigned, and Prior - we want Obs)
lake_data = []
for g in lake_results:
    for l in earthaccess.results.DataGranule.data_links(g, access='direct'):
        if "Obs" in l:
            lake_data.append(l)
print(lake_data)

['s3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.zip']

s3_file_obj2 = fs_s3.open(lake_data[0], mode='rb')

with zipfile.ZipFile(s3_file_obj2, 'r') as zip_ref:
    zip_ref.extractall('SWOT_HR_shp')

SWOT_HR_shp2 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.shp') 
SWOT_HR_shp2

	obs_id	lake_id	overlap	time	time_tai	time_str	wse	wse_u	wse_r_u	wse_std	...	iono_c	xovr_cal_c	p_name	p_grand_id	p_max_wse	p_max_area	p_ref_date	p_ref_ds	p_storage	geometry
0	742081R000002	7420470702	93	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	36.934	0.051	0.051	0.159	...	0.0	0.0	no_data	-99999999	-1.000000e+12	1.35	-9999	-9999.0	-1.000000e+12	POLYGON ((-92.75926 42.04142, -92.75977 42.041...
1	742081R000003	7420472462	75	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	37.037	0.080	0.080	0.143	...	0.0	0.0	no_data	-99999999	-1.000000e+12	1.62	-9999	-9999.0	-1.000000e+12	POLYGON ((-92.91651 42.01167, -92.91681 42.011...
2	742081R000008	7420473212	58	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	36.578	0.181	0.181	0.058	...	0.0	0.0	HENDRICKSON MARSH LAKE	-99999999	-1.000000e+12	45.94	-9999	-9999.0	-1.000000e+12	POLYGON ((-93.24060 41.93319, -93.24066 41.933...
3	742081R000009	7420470712	73	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	36.910	0.110	0.110	0.136	...	0.0	0.0	no_data	-99999999	-1.000000e+12	4.50	-9999	-9999.0	-1.000000e+12	POLYGON ((-92.72557 42.03424, -92.72560 42.034...
4	742081R000011	7420470582	76	7.145116e+08	7.145116e+08	2022-08-22T19:26:51	36.904	0.109	0.109	0.628	...	0.0	0.0	no_data	-99999999	-1.000000e+12	1.89	-9999	-9999.0	-1.000000e+12	POLYGON ((-93.39929 41.90871, -93.39945 41.908...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
17240	742070R000729	7420857422	92	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	33.186	0.098	0.098	0.056	...	0.0	0.0	no_data	-99999999	-1.000000e+12	14.94	-9999	-9999.0	-1.000000e+12	POLYGON ((-95.07581 47.57853, -95.07587 47.578...
17241	742070R000730	7420848152	96	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	33.071	0.038	0.038	0.174	...	0.0	0.0	no_data	-99999999	-1.000000e+12	3.06	-9999	-9999.0	-1.000000e+12	POLYGON ((-94.88532 47.61669, -94.88577 47.616...
17242	712070R000731	7120812272	74	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	33.104	0.077	0.077	0.085	...	0.0	0.0	no_data	-99999999	-1.000000e+12	4.41	-9999	-9999.0	-1.000000e+12	POLYGON ((-95.08313 47.57179, -95.08341 47.571...
17243	712070R000732	7120816202	67	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	32.713	0.106	0.106	0.198	...	0.0	0.0	MUD LAKE	-99999999	-1.000000e+12	6.30	-9999	-9999.0	-1.000000e+12	POLYGON ((-95.39968 47.51004, -95.39975 47.510...
17244	742070R000733	7420857422	95	7.145115e+08	7.145115e+08	2022-08-22T19:25:10	32.725	0.093	0.093	0.119	...	0.0	0.0	no_data	-99999999	-1.000000e+12	14.94	-9999	-9999.0	-1.000000e+12	POLYGON ((-95.07228 47.57473, -95.07257 47.574...

17245 rows × 43 columns

fig, ax = plt.subplots(figsize=(7,12))
SWOT_HR_shp2.plot(ax=ax, color='black')

<Axes: >

3. Water Mask Pixel Cloud NetCDF

Accessing the remaining files is different than the shp files above. We do not need to unzip the files because they are stored in native netCDF files in the cloud. For the rest of the products, we will open via xarray.

watermask_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))

Granules found: 1

The pixel cloud netCDF files are formatted with three groups titled, “pixel cloud”, “tvp”, or “noise” (more detail here). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset.

ds_PIXC = xr.open_mfdataset(earthaccess.open([watermask_results[0]]), group = 'pixel_cloud', engine='h5netcdf')
ds_PIXC

 Opening 1 granules, approx size: 0.0 GB

<xarray.Dataset>
Dimensions:                                (points: 489673, complex_depth: 2)
Coordinates:
    latitude                               (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
    longitude                              (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
Dimensions without coordinates: points, complex_depth
Data variables: (12/49)
    azimuth_index                          (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
    range_index                            (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
    interferogram                          (points, complex_depth) float32 dask.array<chunksize=(489673, 2), meta=np.ndarray>
    power_plus_y                           (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    power_minus_y                          (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    coherent_power                         (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    ...                                     ...
    solid_earth_tide                       (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    load_tide_fes                          (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    load_tide_got                          (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    pole_tide                              (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    ancillary_surface_classification_flag  (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    pixc_qual                              (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
Attributes:
    description:                 cloud of geolocated interferogram pixels
    interferogram_size_azimuth:  2923
    interferogram_size_range:    4575
    looks_to_efflooks:           1.75

xarray.Dataset

• Dimensions:
  • points: 489673
  • complex_depth: 2
• Coordinates: (2)
  • latitude
    (points)
    float64
    dask.array<chunksize=(489673,), meta=np.ndarray>

    long_name :

    latitude (positive N, negative S)

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -80.0

    valid_max :

    80.0

    comment :

    Geodetic latitude [-80,80] (degrees north of equator) of the pixel.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    longitude

    (points)

    float64

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    longitude (degrees East)

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    comment :

    Longitude [-180,180) (east of the Greenwich meridian) of the pixel.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    Data variables: (49)

    azimuth_index

    (points)

    float64

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    rare interferogram azimuth index

    units :

    1

    valid_min :

    0

    valid_max :

    999999

    comment :

    Rare interferogram azimuth index (indexed from 0).

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    range_index

    (points)

    float64

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    rare interferogram range index

    units :

    1

    valid_min :

    0

    valid_max :

    999999

    comment :

    Rare interferogram range index (indexed from 0).

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    interferogram

    (points, complex_depth)

    float32

    dask.array<chunksize=(489673, 2), meta=np.ndarray>

    

    long_name :

    rare interferogram

    units :

    1

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Complex unflattened rare interferogram.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673, 2) (489673, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    power_plus_y

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    power for plus_y channel

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Power for the plus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    power_minus_y

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    power for minus_y channel

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Power for the minus_y channel (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    coherent_power

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    coherent power combination of minus_y and plus_y channels

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Power computed by combining the plus_y and minus_y channels coherently by co-aligning the phases (arbitrary units that give sigma0 when noise subtracted and normalized by the X factor).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    x_factor_plus_y

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    X factor for plus_y channel power

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    X factor for the plus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    x_factor_minus_y

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    X factor for minus_y channel power

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    X factor for the minus_y channel power in linear units (arbitrary units to normalize noise-subtracted power to sigma0).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    water_frac

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    water fraction

    units :

    1

    valid_min :

    -1000.0

    valid_max :

    10000.0

    comment :

    Noisy estimate of the fraction of the pixel that is water.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    water_frac_uncert

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    water fraction uncertainty

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Uncertainty estimate of the water fraction estimate (width of noisy water frac estimate distribution).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    classification

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    classification

    flag_meanings :

    land land_near_water water_near_land open_water land_near_dark_water dark_water_edge dark_water

    flag_values :

    [ 1 2 3 4 22 23 24]

    valid_min :

    1

    valid_max :

    24

    comment :

    Flags indicating water detection results.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    false_detection_rate

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    false detection rate

    units :

    1

    valid_min :

    0.0

    valid_max :

    1.0

    comment :

    Probability of falsely detecting water when there is none.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    missed_detection_rate

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    missed detection rate

    units :

    1

    valid_min :

    0.0

    valid_max :

    1.0

    comment :

    Probability of falsely detecting no water when there is water.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    prior_water_prob

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    prior water probability

    units :

    1

    valid_min :

    0.0

    valid_max :

    1.0

    comment :

    Prior probability of water occurring.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    bright_land_flag

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    bright land flag

    standard_name :

    status_flag

    flag_meanings :

    not_bright_land bright_land bright_land_or_water

    flag_values :

    [0 1 2]

    valid_min :

    0

    valid_max :

    2

    comment :

    Flag indicating areas that are not typically water but are expected to be bright (e.g., urban areas, ice). Flag value 2 indicates cases where prior data indicate land, but where prior_water_prob indicates possible water.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    layover_impact

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    layover impact

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Estimate of the height error caused by layover, which may not be reliable on a pixel by pixel basis, but may be useful to augment aggregated height uncertainties.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    eff_num_rare_looks

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    effective number of rare looks

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Effective number of independent looks taken to form the rare interferogram.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    height

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    height above reference ellipsoid

    units :

    m

    valid_min :

    -1500.0

    valid_max :

    15000.0

    comment :

    Height of the pixel above the reference ellipsoid.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    cross_track

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    approximate cross-track location

    units :

    m

    valid_min :

    -75000.0

    valid_max :

    75000.0

    comment :

    Approximate cross-track location of the pixel.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    pixel_area

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    pixel area

    units :

    m^2

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Pixel area.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    inc

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    incidence angle

    units :

    degrees

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Incidence angle.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    phase_noise_std

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    phase noise standard deviation

    units :

    radians

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Estimate of the phase noise standard deviation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    dlatitude_dphase

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    sensitivity of latitude estimate to interferogram phase

    units :

    degrees/radian

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the latitude estimate to the interferogram phase.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    dlongitude_dphase

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    sensitivity of longitude estimate to interferogram phase

    units :

    degrees/radian

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the longitude estimate to the interferogram phase.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    dheight_dphase

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    sensitivity of height estimate to interferogram phase

    units :

    m/radian

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the interferogram phase.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    dheight_droll

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    sensitivity of height estimate to spacecraft roll

    units :

    m/degrees

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the spacecraft roll.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    dheight_dbaseline

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    sensitivity of height estimate to interferometric baseline

    units :

    m/m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the interferometric baseline.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    dheight_drange

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    sensitivity of height estimate to range (delay)

    units :

    m/m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the height estimate to the range (delay).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    darea_dheight

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    sensitivity of pixel area to reference height

    units :

    m^2/m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Sensitivity of the pixel area to the reference height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    illumination_time

    (points)

    datetime64[ns]

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    time of illumination of each pixel (UTC)

    standard_name :

    time

    tai_utc_difference :

    [Value of TAI-UTC at time of first record]

    leap_second :

    YYYY-MM-DD hh:mm:ss

    comment :

    Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

    illumination_time_tai

    (points)

    datetime64[ns]

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    time of illumination of each pixel (TAI)

    standard_name :

    time

    comment :

    Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

    eff_num_medium_looks

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    effective number of medium looks

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    Effective number of independent looks taken in forming the medium interferogram (after adaptive averaging).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    sig0

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    sigma0

    units :

    1

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    phase_unwrapping_region

    (points)

    float64

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    phase unwrapping region index

    units :

    1

    valid_min :

    -1

    valid_max :

    99999999

    comment :

    Phase unwrapping region index.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    instrument_range_cor

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    instrument range correction

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to range before geolocation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    instrument_phase_cor

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    instrument phase correction

    units :

    radians

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to phase before geolocation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    instrument_baseline_cor

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    instrument baseline correction

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to baseline before geolocation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    instrument_attitude_cor

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    instrument attitude correction

    units :

    degrees

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Term that incorporates all calibration corrections applied to attitude before geolocation.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    model_dry_tropo_cor

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    dry troposphere vertical correction

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m

    valid_min :

    -3.0

    valid_max :

    -1.5

    comment :

    Equivalent vertical correction due to dry troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    model_wet_tropo_cor

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    wet troposphere vertical correction

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m

    valid_min :

    -1.0

    valid_max :

    0.0

    comment :

    Equivalent vertical correction due to wet troposphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    iono_cor_gim_ka

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    ionosphere vertical correction

    source :

    Global Ionosphere Maps

    institution :

    JPL

    units :

    m

    valid_min :

    -0.5

    valid_max :

    0.0

    comment :

    Equivalent vertical correction due to ionosphere delay. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported pixel height results in the uncorrected pixel height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    height_cor_xover

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    height correction from KaRIn crossovers

    units :

    m

    valid_min :

    -10.0

    valid_max :

    10.0

    comment :

    Height correction from KaRIn crossover calibration. The correction is applied before geolocation but reported as an equivalent height correction.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    geoid

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    geoid height

    standard_name :

    geoid_height_above_reference_ellipsoid

    source :

    EGM2008 (Pavlis et al., 2012)

    units :

    m

    valid_min :

    -150.0

    valid_max :

    150.0

    comment :

    Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    solid_earth_tide

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    solid Earth tide height

    source :

    Cartwright and Taylor (1971) and Cartwright and Edden (1973)

    units :

    m

    valid_min :

    -1.0

    valid_max :

    1.0

    comment :

    Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    load_tide_fes

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    geocentric load tide height (FES)

    source :

    FES2014b (Carrere et al., 2016)

    institution :

    LEGOS/CNES

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    load_tide_got

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    geocentric load tide height (GOT)

    source :

    GOT4.10c (Ray, 2013)

    institution :

    GSFC

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    pole_tide

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    geocentric pole tide height

    source :

    Wahr (1985) and Desai et al. (2015)

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth's crust).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    ancillary_surface_classification_flag

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    long_name :

    surface classification

    standard_name :

    status_flag

    source :

    MODIS/GlobCover

    institution :

    European Space Agency

    flag_meanings :

    open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin

    flag_values :

    [0 1 2 3 4 5 6]

    valid_min :

    0

    valid_max :

    6

    comment :

    7-state surface type classification computed from a mask built with MODIS and GlobCover data.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    pixc_qual

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    standard_name :

    status_flag

    flag_meanings :

    good bad

    flag_values :

    [0 1]

    valid_min :

    0

    valid_max :

    1

    comment :

    Quality flag for pixel cloud data

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (0)
• Attributes: (4)

  description :

  cloud of geolocated interferogram pixels

  interferogram_size_azimuth :

  2923

  interferogram_size_range :

  4575

  looks_to_efflooks :

  1.75

plt.scatter(x=ds_PIXC.longitude, y=ds_PIXC.latitude, c=ds_PIXC.height)
plt.colorbar().set_label('Height (m)')

4. Water Mask Pixel Cloud Vector Attribute NetCDF

vector_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))

Granules found: 1

ds_PIXCVEC = xr.open_mfdataset(earthaccess.open([vector_results[0]]), decode_cf=False,  engine='h5netcdf')
ds_PIXCVEC

 Opening 1 granules, approx size: 0.0 GB

<xarray.Dataset>
Dimensions:               (points: 489673, nchar_reach_id: 11,
                           nchar_node_id: 14, nchar_lake_id: 10,
                           nchar_obs_id: 13)
Dimensions without coordinates: points, nchar_reach_id, nchar_node_id,
                                nchar_lake_id, nchar_obs_id
Data variables:
    azimuth_index         (points) int32 dask.array<chunksize=(489673,), meta=np.ndarray>
    range_index           (points) int32 dask.array<chunksize=(489673,), meta=np.ndarray>
    latitude_vectorproc   (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
    longitude_vectorproc  (points) float64 dask.array<chunksize=(489673,), meta=np.ndarray>
    height_vectorproc     (points) float32 dask.array<chunksize=(489673,), meta=np.ndarray>
    reach_id              (points, nchar_reach_id) |S1 dask.array<chunksize=(489673, 11), meta=np.ndarray>
    node_id               (points, nchar_node_id) |S1 dask.array<chunksize=(489673, 14), meta=np.ndarray>
    lake_id               (points, nchar_lake_id) |S1 dask.array<chunksize=(489673, 10), meta=np.ndarray>
    obs_id                (points, nchar_obs_id) |S1 dask.array<chunksize=(489673, 13), meta=np.ndarray>
    ice_clim_f            (points) int8 dask.array<chunksize=(489673,), meta=np.ndarray>
    ice_dyn_f             (points) int8 dask.array<chunksize=(489673,), meta=np.ndarray>
Attributes: (12/36)
    Conventions:                                 CF-1.7
    title:                                       Level 2 KaRIn high rate pixe...
    institution:                                 CNES
    source:                                      Simulation
    history:                                     2021-04-14 18:11:49Z: Creation
    platform:                                    SWOT
    ...                                          ...
    xref_input_l2_hr_pixc_vec_river_file:        /work/ALT/swot/swotdev/desro...
    xref_static_river_db_file:                   
    xref_static_lake_db_file:                    /work/ALT/swot/swotpub/BD/BD...
    xref_l2_hr_lake_tile_config_parameter_file:  /work/ALT/swot/swotdev/desro...
    ellipsoid_semi_major_axis:                   6371008.771416667
    ellipsoid_flattening:                        0.0

xarray.Dataset

• Dimensions:
  • points: 489673
  • nchar_reach_id: 11
  • nchar_node_id: 14
  • nchar_lake_id: 10
  • nchar_obs_id: 13
• Coordinates: (0)
• Data variables: (11)
  • azimuth_index
    (points)
    int32
    dask.array<chunksize=(489673,), meta=np.ndarray>

    _FillValue :

    2147483647

    long_name :

    rare interferogram azimuth index

    units :

    1

    valid_min :

    0

    valid_max :

    999999

    coordinates :

    longitude_vectorproc latitude_vectorproc

    comment :

    Rare interferogram azimuth index (indexed from 0).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

    range_index

    (points)

    int32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    _FillValue :

    2147483647

    long_name :

    rare interferogram range index

    units :

    1

    valid_min :

    0

    valid_max :

    999999

    coordinates :

    longitude_vectorproc latitude_vectorproc

    comment :

    Rare interferogram range index (indexed from 0).

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type int32 numpy.ndarray

    latitude_vectorproc

    (points)

    float64

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    _FillValue :

    9.969209968386869e+36

    long_name :

    height-constrained geolocation latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -80.0

    valid_max :

    80.0

    comment :

    Height-constrained geodetic latitude of the pixel. Units are in degrees north of the equator.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    longitude_vectorproc

    (points)

    float64

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    _FillValue :

    9.969209968386869e+36

    long_name :

    height-constrained geolocation longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    comment :

    Height-constrained geodetic longitude of the pixel. Positive=degrees east of the Greenwich meridian. Negative=degrees west of the Greenwich meridian.

    Array Chunk Bytes 3.74 MiB 3.74 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    height_vectorproc

    (points)

    float32

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    _FillValue :

    9.96921e+36

    long_name :

    height above reference ellipsoid

    units :

    m

    valid_min :

    -1500.0

    valid_max :

    15000.0

    coordinates :

    longitude_vectorproc latitude_vectorproc

    comment :

    Height-constrained height of the pixel above the reference ellipsoid.

    Array Chunk Bytes 1.87 MiB 1.87 MiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    reach_id

    (points, nchar_reach_id)

    |S1

    dask.array<chunksize=(489673, 11), meta=np.ndarray>

    

    long_name :

    identifier of the associated prior river reach

    coordinates :

    longitude_vectorproc latitude_vectorproc

    comment :

    Unique reach identifier from the prior river database. The format of the identifier is CBBBBBRRRRT, where C=continent, B=basin, R=reach, T=type.

    Array Chunk Bytes 5.14 MiB 5.14 MiB Shape (489673, 11) (489673, 11) Dask graph 1 chunks in 2 graph layers Data type |S1 numpy.ndarray

    node_id

    (points, nchar_node_id)

    |S1

    dask.array<chunksize=(489673, 14), meta=np.ndarray>

    

    long_name :

    identifier of the associated prior river node

    coordinates :

    longitude_vectorproc latitude_vectorproc

    comment :

    Unique node identifier from the prior river database. The format of the identifier is CBBBBBRRRRNNNT, where C=continent, B=basin, R=reach, N=node, T=type of water body.

    Array Chunk Bytes 6.54 MiB 6.54 MiB Shape (489673, 14) (489673, 14) Dask graph 1 chunks in 2 graph layers Data type |S1 numpy.ndarray

    lake_id

    (points, nchar_lake_id)

    |S1

    dask.array<chunksize=(489673, 10), meta=np.ndarray>

    

    long_name :

    identifier of the associated prior lake

    coordinates :

    longitude_vectorproc latitude_vectorproc

    comment :

    Identifier of the lake from the lake prior database) associated to the pixel. The format of the identifier is CBBNNNNNNT, where C=continent, B=basin, N=counter within the basin, T=type of water body.

    Array Chunk Bytes 4.67 MiB 4.67 MiB Shape (489673, 10) (489673, 10) Dask graph 1 chunks in 2 graph layers Data type |S1 numpy.ndarray

    obs_id

    (points, nchar_obs_id)

    |S1

    dask.array<chunksize=(489673, 13), meta=np.ndarray>

    

    long_name :

    identifier of the observed feature

    coordinates :

    longitude_vectorproc latitude_vectorproc

    comment :

    Tile-specific identifier of the observed feature associated to the pixel. The format of the identifier is CBBTTTSNNNNNN, where C=continent, B=basin, T=tile number, S=swath side, N=lake counter within the PIXC tile.

    Array Chunk Bytes 6.07 MiB 6.07 MiB Shape (489673, 13) (489673, 13) Dask graph 1 chunks in 2 graph layers Data type |S1 numpy.ndarray

    ice_clim_f

    (points)

    int8

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    _FillValue :

    127

    long_name :

    climatological ice cover flag

    flag_meanings :

    no_ice_cover partial_ice_cover full_ice_cover

    flag_values :

    [0 1 2]

    institution :

    University of North Carolina

    coordinates :

    longitude_vectorproc latitude_vectorproc

    comment :

    Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.

    Array Chunk Bytes 478.20 kiB 478.20 kiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

    ice_dyn_f

    (points)

    int8

    dask.array<chunksize=(489673,), meta=np.ndarray>

    

    _FillValue :

    127

    long_name :

    dynamical ice cover flag

    flag_meanings :

    no_ice_cover partial_ice_cover full_ice_cover

    flag_values :

    [0 1 2]

    institution :

    University of North Carolina

    coordinates :

    longitude_vectorproc latitude_vectorproc

    comment :

    Dynamic ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the surface is not ice covered, partially ice covered, and fully ice covered, respectively. A value of 255 indicates that this flag is not available.

    Array Chunk Bytes 478.20 kiB 478.20 kiB Shape (489673,) (489673,) Dask graph 1 chunks in 2 graph layers Data type int8 numpy.ndarray

• Indexes: (0)
• Attributes: (36)

  Conventions :

  CF-1.7

  title :

  Level 2 KaRIn high rate pixel cloud vector attribute product

  institution :

  CNES

  source :

  Simulation

  history :

  2021-04-14 18:11:49Z: Creation

  platform :

  SWOT

  references :

  0.2

  reference_document :

  SWOT-TN-CDM-0677-CNES

  contact :

  test@cnes.fr

  cycle_number :

  7

  pass_number :

  522

  tile_number :

  93

  swath_side :

  L

  tile_name :

  522_093L

  continent :

  NA

  time_coverage_start :

  2022-08-22 19:28:50.964042Z

  time_coverage_end :

  2022-08-22 19:29:00.951707Z

  geospatial_lon_min :

  -90.50978392584558

  geospatial_lon_max :

  -89.62391069818588

  geospatial_lat_min :

  34.79938523279299

  geospatial_lat_max :

  35.48806356833591

  inner_first_longitude :

  -90.50978392584558

  inner_first_latitude :

  35.362946480544466

  inner_last_longitude :

  -90.36177519926197

  inner_last_latitude :

  34.79938523279299

  outer_first_longitude :

  -89.76709094457013

  outer_first_latitude :

  35.48806356833591

  outer_last_longitude :

  -89.62391069818588

  outer_last_latitude :

  34.92312785225805

  xref_input_l2_hr_pixc_file :

  /work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/simu/SWOT_L2_HR_PIXC_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc

  xref_input_l2_hr_pixc_vec_river_file :

  /work/ALT/swot/swotdev/desrochesd/swot-hydrology-toolbox/test/sample_dataset_us/output/river/SWOT_L2_HR_PIXCVecRiver_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc

  xref_static_river_db_file :

  xref_static_lake_db_file :

  /work/ALT/swot/swotpub/BD/BD_lakes/PLD

  xref_l2_hr_lake_tile_config_parameter_file :

  /work/ALT/swot/swotdev/desrochesd/swot-sds-16-10/swotCNES/PGE/lake_tile/lake_tile_param.cfg

  ellipsoid_semi_major_axis :

  6371008.771416667

  ellipsoid_flattening :

  0.0

pixcvec_htvals = ds_PIXCVEC.height_vectorproc.compute()
pixcvec_latvals = ds_PIXCVEC.latitude_vectorproc.compute()
pixcvec_lonvals = ds_PIXCVEC.longitude_vectorproc.compute()

#Before plotting, we set all fill values to nan so that the graph shows up better spatially
pixcvec_htvals[pixcvec_htvals > 15000] = np.nan
pixcvec_latvals[pixcvec_latvals > 80] = np.nan
pixcvec_lonvals[pixcvec_lonvals > 180] = np.nan

plt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)
plt.colorbar().set_label('Height (m)')

5. Raster NetCDF

raster_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1', temporal = ('2022-08-22 19:28:50', '2022-08-22 19:29:11'), point = ('-90', '35'))

Granules found: 2

#this collection has 100m and 250m granules, but we only want 100m
raster_data = []
for g in raster_results:
    for l in earthaccess.results.DataGranule.data_links(g, access='direct'):
        if "100m" in l:
            raster_data.append(l)
print(raster_data)

['s3://podaac-ops-cumulus-protected/SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1/SWOT_L2_HR_Raster_100m_UTM15S_N_x_x_x_007_522_047F_20220822T192850_20220822T192911_Dx0000_01.nc']

ds_raster = xr.open_mfdataset(earthaccess.open([raster_data[0]], provider = 'POCLOUD'), engine='h5netcdf')
ds_raster

<xarray.Dataset>
Dimensions:                (x: 1543, y: 1540)
Coordinates:
  * x                      (x) float64 6.567e+05 6.568e+05 ... 8.109e+05
  * y                      (y) float64 3.775e+06 3.775e+06 ... 3.929e+06
Data variables: (12/30)
    crs                    object ...
    longitude              (y, x) float64 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    latitude               (y, x) float64 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    wse                    (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    wse_uncert             (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    water_area             (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    ...                     ...
    load_tide_fes          (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    load_tide_got          (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    pole_tide              (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    model_dry_tropo_cor    (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    model_wet_tropo_cor    (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
    iono_cor_gim_ka        (y, x) float32 dask.array<chunksize=(1540, 1543), meta=np.ndarray>
Attributes: (12/45)
    Conventions:                     CF-1.7
    title:                           Level 2 KaRIn High Rate Raster Data Product
    institution:                     JPL
    source:                          Large scale simulator
    history:                         2021-09-08T22:28:33Z : Creation
    mission_name:                    SWOT
    ...                              ...
    utm_zone_num:                    15
    mgrs_latitude_band:              S
    x_min:                           656700.0
    x_max:                           810900.0
    y_min:                           3775000.0
    y_max:                           3928900.0

xarray.Dataset

• Dimensions:
  • x: 1543
  • y: 1540
• Coordinates: (2)
  • x
    (x)
    float64
    6.567e+05 6.568e+05 ... 8.109e+05

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    units :

    m

    valid_min :

    -10000000.0

    valid_max :

    10000000.0

    comment :

    UTM easting coordinate of the pixel.

    array([656700., 656800., 656900., ..., 810700., 810800., 810900.])

  • y
    (y)
    float64
    3.775e+06 3.775e+06 ... 3.929e+06

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    units :

    m

    valid_min :

    -20000000.0

    valid_max :

    20000000.0

    comment :

    UTM northing coordinate of the pixel.

    array([3775000., 3775100., 3775200., ..., 3928700., 3928800., 3928900.])

• Data variables: (30)
  • crs
    ()
    object
    ...

    long_name :

    CRS Definition

    grid_mapping_name :

    transverse_mercator

    projected_crs_name :

    WGS 84 / UTM zone 15N

    geographic_crs_name :

    WGS 84

    reference_ellipsoid_name :

    WGS 84

    horizontal_datum_name :

    WGS_1984

    prime_meridian_name :

    Greenwich

    false_easting :

    500000.0

    false_northing :

    0.0

    longitude_of_central_meridian :

    -93.0

    longitude_of_prime_meridian :

    0.0

    latitude_of_projection_origin :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    semi_major_axis :

    6378137.0

    inverse_flattening :

    298.257223563

    crs_wkt :

    PROJCS["WGS 84 / UTM zone 15N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-93],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32615"]]

    spatial_ref :

    PROJCS["WGS 84 / UTM zone 15N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-93],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32615"]]

    comment :

    UTM zone coordinate reference system.

    [1 values with dtype=object]

  • longitude
    (y, x)
    float64
    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    long_name :

    longitude (degrees East)

    standard_name :

    longitude

    grid_mapping :

    crs

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    comment :

    Longitude [-180,180) (east of the Greenwich meridian) of the pixel.

    Array Chunk Bytes 18.13 MiB 18.13 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    latitude

    (y, x)

    float64

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    latitude (positive N, negative S)

    standard_name :

    latitude

    grid_mapping :

    crs

    units :

    degrees_north

    valid_min :

    -80.0

    valid_max :

    80.0

    comment :

    Latitude [-80,80] (degrees north of equator) of the pixel.

    Array Chunk Bytes 18.13 MiB 18.13 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    wse

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    water surface elevation above geoid

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -1500.0

    valid_max :

    15000.0

    comment :

    Water surface elevation of the pixel above the geoid and after using models to subtract the effects of tides (solid_earth_tide, load_tide_fes, pole_tide).

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    wse_uncert

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    uncertainty in the water surface elevation

    grid_mapping :

    crs

    units :

    m

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    1-sigma uncertainty in the water surface elevation.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    water_area

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    surface area of water

    grid_mapping :

    crs

    units :

    m^2

    valid_min :

    -2000000.0

    valid_max :

    2000000000.0

    comment :

    Surface area of the water pixels.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    water_area_uncert

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    uncertainty in the water surface area

    grid_mapping :

    crs

    units :

    m^2

    valid_min :

    0.0

    valid_max :

    2000000000.0

    comment :

    1-sigma uncertainty in the water surface area

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    water_frac

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    water fraction

    grid_mapping :

    crs

    units :

    1

    valid_min :

    -1000.0

    valid_max :

    10000.0

    comment :

    Fraction of the pixel that is water.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    water_frac_uncert

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    uncertainty in the water fraction

    grid_mapping :

    crs

    units :

    1

    valid_min :

    0.0

    valid_max :

    999999.0

    comment :

    1-sigma uncertainty in the water fraction.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    sig0

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    sigma0

    grid_mapping :

    crs

    units :

    1

    valid_min :

    -1000.0

    valid_max :

    10000000.0

    comment :

    Normalized radar cross section (sigma0) in real, linear units (not decibels). The value may be negative due to noise subtraction.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    sig0_uncert

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    uncertainty in sigma0

    grid_mapping :

    crs

    units :

    1

    valid_min :

    0.0

    valid_max :

    1000.0

    comment :

    1-sigma uncertainty in sigma0. The value is provided in linear units. This value is a one-sigma additive (not multiplicative) uncertainty term, which can be added to or subtracted from sigma0.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    inc

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    incidence angle

    grid_mapping :

    crs

    units :

    degrees

    valid_min :

    0.0

    valid_max :

    90.0

    comment :

    Incidence angle.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    cross_track

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    approximate cross-track location

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -75000.0

    valid_max :

    75000.0

    comment :

    Approximate cross-track location of the pixel.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    illumination_time

    (y, x)

    datetime64[ns]

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    time of illumination of each pixel (UTC)

    standard_name :

    time

    tai_utc_difference :

    -32.0

    leap_second :

    YYYY-MM-DDThh:mm:ssZ

    comment :

    Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.

    Array Chunk Bytes 18.13 MiB 18.13 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

    illumination_time_tai

    (y, x)

    datetime64[ns]

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    time of illumination of each pixel (TAI)

    standard_name :

    time

    comment :

    Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [illumination_time:tai_utc_difference].

    Array Chunk Bytes 18.13 MiB 18.13 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

    raster_qual

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    standard_name :

    status_flag

    grid_mapping :

    crs

    flag_meanings :

    good bad

    flag_values :

    [0 1]

    valid_min :

    0

    valid_max :

    1

    comment :

    Quality flag for raster data.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    n_wse_pix

    (y, x)

    float64

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    number of wse pixels

    grid_mapping :

    crs

    units :

    l

    valid_min :

    0

    valid_max :

    999999

    comment :

    Number of pixel cloud samples used in water surface elevation aggregation.

    Array Chunk Bytes 18.13 MiB 18.13 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    n_area_pix

    (y, x)

    float64

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    number of area pixels

    grid_mapping :

    crs

    units :

    l

    valid_min :

    0

    valid_max :

    999999

    comment :

    Number of pixel cloud samples used in water area and water fraction aggregation.

    Array Chunk Bytes 18.13 MiB 18.13 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

    dark_frac

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    fractional area of dark water

    grid_mapping :

    crs

    units :

    l

    valid_min :

    -1000.0

    valid_max :

    10000.0

    comment :

    Fraction of pixel water area covered by dark water.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    ice_clim_flag

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    climatological ice cover flag

    source :

    UNC

    grid_mapping :

    crs

    flag_meanings :

    no_ice_cover uncertain_ice_cover full_ice_cover

    flag_values :

    [0 1 2]

    valid_min :

    0

    valid_max :

    2

    comment :

    Climatological ice cover flag indicating whether the pixel is ice-covered on the day of the observation based on external climatological information (not the SWOT measurement). Values of 0, 1, and 2 indicate that the pixel is likely not ice covered, may or may not be partially or fully ice covered, and likely fully ice covered, respectively.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    ice_dyn_flag

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    dynamic ice cover flag

    source :

    UNC

    grid_mapping :

    crs

    flag_meanings :

    no_ice_cover partial_ice_cover full_ice_cover

    flag_values :

    [0 1 2]

    valid_min :

    0

    valid_max :

    2

    comment :

    Dynamic ice cover flag indicating whether the surface is ice-covered on the day of the observation based on analysis of external satellite optical data. Values of 0, 1, and 2 indicate that the pixel is not ice covered, partially ice covered, and fully ice covered, respectively.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    layover_impact

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    layover impact

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -999999.0

    valid_max :

    999999.0

    comment :

    Estimate of the water surface elevation error caused by layover.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    geoid

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    geoid height

    standard_name :

    geoid_height_above_reference_ellipsoid

    source :

    EGM2008 (Pavlis et al., 2012)

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -150.0

    valid_max :

    150.0

    comment :

    Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    solid_earth_tide

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    solid Earth tide height

    source :

    Cartwright and Taylor (1971) and Cartwright and Edden (1973)

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -1.0

    valid_max :

    1.0

    comment :

    Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    load_tide_fes

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    geocentric load tide height (FES)

    source :

    FES2014b (Carrere et al., 2016)

    institution :

    LEGOS/CNES

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    load_tide_got

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    geocentric load tide height (GOT)

    source :

    GOT4.10c (Ray, 2013)

    institution :

    GSFC

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth’s crust. This value is reported for reference but is not applied to the reported height.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    pole_tide

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    geocentric pole tide height

    source :

    Wahr (1985) and Desai et al. (2015)

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -0.2

    valid_max :

    0.2

    comment :

    Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth’s crust).

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    model_dry_tropo_cor

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    dry troposphere vertical correction

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -3.0

    valid_max :

    -1.5

    comment :

    Equivalent vertical correction due to dry troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    model_wet_tropo_cor

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    wet troposphere vertical correction

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -1.0

    valid_max :

    0.0

    comment :

    Equivalent vertical correction due to wet troposphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

    iono_cor_gim_ka

    (y, x)

    float32

    dask.array<chunksize=(1540, 1543), meta=np.ndarray>

    

    long_name :

    ionosphere vertical correction

    source :

    Global Ionosphere Maps

    institution :

    JPL

    grid_mapping :

    crs

    units :

    m

    valid_min :

    -0.5

    valid_max :

    0.0

    comment :

    Equivalent vertical correction due to ionosphere delay. The reported water surface elevation, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported water surface elevation results in the uncorrected pixel height.

    Array Chunk Bytes 9.06 MiB 9.06 MiB Shape (1540, 1543) (1540, 1543) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([656700.0, 656800.0, 656900.0, 657000.0, 657100.0, 657200.0, 657300.0,
           657400.0, 657500.0, 657600.0,
           ...
           810000.0, 810100.0, 810200.0, 810300.0, 810400.0, 810500.0, 810600.0,
           810700.0, 810800.0, 810900.0],
          dtype='float64', name='x', length=1543))

  • y
    PandasIndex

    PandasIndex(Index([3775000.0, 3775100.0, 3775200.0, 3775300.0, 3775400.0, 3775500.0,
           3775600.0, 3775700.0, 3775800.0, 3775900.0,
           ...
           3928000.0, 3928100.0, 3928200.0, 3928300.0, 3928400.0, 3928500.0,
           3928600.0, 3928700.0, 3928800.0, 3928900.0],
          dtype='float64', name='y', length=1540))

• Attributes: (45)

  Conventions :

  CF-1.7

  title :

  Level 2 KaRIn High Rate Raster Data Product

  institution :

  JPL

  source :

  Large scale simulator

  history :

  2021-09-08T22:28:33Z : Creation

  mission_name :

  SWOT

  references :

  https://github.com/SWOTAlgorithms/Raster-Processor

  reference_document :

  JPL D-56416 - Revision A (DRAFT) - November 5, 2020

  contact :

  alexander.t.corben[at]jpl.nasa.gov

  cycle_number :

  7

  pass_number :

  522

  scene_number :

  47

  tile_numbers :

  [92 93 94 95 92 93 94 95]

  tile_names :

  522_092L, 522_093L, 522_094L, 522_095L, 522_092R, 522_093R, 522_094R, 522_095R

  tile_polarizations :

  V, V, V, V, V, V, V, V

  coordinate_reference_system :

  Universal Transverse Mercator

  resolution :

  100.0

  short_name :

  L2_HR_Raster

  descriptor_string :

  100m_UTM15S_N_x_x_x

  crid :

  Dx0000

  product_version :

  V0.1

  pge_name :

  adt_pge_standin

  pge_version :

  V0.1

  time_coverage_start :

  2022-08-22 19:28:50.964042Z

  time_coverage_end :

  2022-08-22 19:29:10.946208Z

  geospatial_lon_min :

  -91.27757002156555

  geospatial_lon_max :

  -89.62061588835118

  geospatial_lat_min :

  34.09943218249787

  geospatial_lat_max :

  35.464214684504334

  left_first_longitude :

  -89.89843338760357

  left_first_latitude :

  35.464214684504334

  left_last_longitude :

  -89.62061588835118

  left_last_latitude :

  34.33243031374548

  right_first_longitude :

  -91.27757002156555

  right_first_latitude :

  35.22613283570163

  right_last_longitude :

  -90.98228790375923

  right_last_latitude :

  34.09943218249787

  xref_input_l2_hr_pixc_files :

  SWOT_L2_HR_PIXC_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXC_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.nc

  xref_input_l2_hr_pixcvec_files :

  SWOT_L2_HR_PIXCVec_007_522_092L_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093L_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094L_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095L_20220822T192910_20220822T192921_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_092R_20220822T192840_20220822T192851_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_093R_20220822T192850_20220822T192901_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_094R_20220822T192900_20220822T192911_Dx0000_01.nc, SWOT_L2_HR_PIXCVec_007_522_095R_20220822T192910_20220822T192921_Dx0000_01.nc

  utm_zone_num :

  15

  mgrs_latitude_band :

  S

  x_min :

  656700.0

  x_max :

  810900.0

  y_min :

  3775000.0

  y_max :

  3928900.0

It’s easy to analyze and plot the data with packages such as hvplot!

ds_raster.wse.hvplot.image(y='y', x='x')